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Dans-DiscountModels/12b-mn-dans-personality-engine-v1.3.0-TestArticle-1

sourceHugging Faceapache-2.0updated 1y agoView on Hugging Face
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Model Card

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/> <details><summary>See axolotl config</summary>

axolotl version: 0.10.0.dev0

yaml
base_model: Dans-DiscountModels/Mistral-Nemo-Base-2407-DanChat
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

trust_remote_code:

# wandb configuration
wandb_project: 12b-mn-dans-personality-engine
wandb_watch:

wandb_run_id: V1.3.0-1-4 # V{Version}-{Run Number}-{Attempt Number}
wandb_log_model:

# push checkpoints to hub
hub_model_id: Dans-DiscountModels/12b-mn-dans-personality-engine-v1.3.0-TestArticle-1
# how to push checkpoints to hub
# https://huggingface.co/docs/transformers/v4.31.0/en/main_classes/trainer#transformers.TrainingArguments.hub_strategy
hub_strategy: "every_save"
# Whether to use hf `use_auth_token` for loading datasets. Useful for fetching private datasets
# Required to be true when used in combination with `push_dataset_to_hub`
hf_use_auth_token: true

# where to save the finished model to
output_dir: ./12b-mn-dans-personality-engine-v1.3.0

# dataset settings (local or huggingface repo)
datasets:
  - path: Dans-DiscountModels/pretokenization-test-5
    ds_type: parquet
    type:

plugins:
  - axolotl.integrations.liger.LigerPlugin
  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
liger_rope: true
liger_rms_norm: true
liger_layer_norm: true
liger_glu_activation: true
liger_fused_linear_cross_entropy: true
cut_cross_entropy: true

load_in_8bit: false
load_in_4bit: false
strict: false

adapter:
lora_model_dir:

dataset_prepared_path: ./12b-mn-dans-personality-engine-data
val_set_size: 0.003

sequence_len: 32768

sample_packing: true
eval_sample_packing: true

pad_to_sequence_len: true

gradient_checkpointing: true

gradient_accumulation_steps: 2
micro_batch_size: 2

num_epochs: 2

optimizer: ademamix_8bit
optim_args: "beta1=0.9,beta2=0.999,beta3=0.999,alpha=5"

lr_scheduler: rex
learning_rate: 0.00001
cosine_min_lr_ratio:

weight_decay:

max_grad_norm: 0.001

train_on_inputs: false
group_by_length: false

bf16: true
fp16: false
tf32: false

early_stopping_patience:

resume_from_checkpoint:
auto_resume_from_checkpoints: true

local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_ratio: 0.1

evals_per_epoch: 24
eval_table_size:
eval_max_new_tokens:

saves_per_epoch: 2
save_total_limit: 1

debug: false

deepspeed: deepspeed_configs/zero3_bf16.json

fsdp:
fsdp_config:

special_tokens:

</details><br>

12b-mn-dans-personality-engine-v1.3.0-TestArticle-1

This model is a fine-tuned version of Dans-DiscountModels/Mistral-Nemo-Base-2407-DanChat on the Dans-DiscountModels/pretokenization-test-5 dataset. It achieves the following results on the evaluation set:

  • —Loss: 1.4392

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • —learning_rate: 1e-05
  • —trainbatchsize: 2
  • —evalbatchsize: 2
  • —seed: 42
  • —distributed_type: multi-GPU
  • —num_devices: 8
  • —gradientaccumulationsteps: 2
  • —totaltrainbatch_size: 32
  • —totalevalbatch_size: 16
  • —optimizer: Use ademamix_8bit and the args are: beta1=0.9,beta2=0.999,beta3=0.999,alpha=5
  • —lrschedulertype: cosine
  • —lrschedulerwarmup_steps: 321
  • —num_epochs: 2.0

Training results

Training LossEpochStepValidation Loss
1.80860.000611.7459
1.5930.0417671.5911
1.55780.08331341.5565
1.57820.12502011.5436
1.57020.16662681.5377
1.59260.20833351.5328
1.63640.24994021.5291
1.50820.29164691.5234
1.60020.33325361.5197
1.52520.37496031.5162
1.59150.41656701.5121
1.51080.45827371.5103
1.56630.49988041.5063
1.50850.54158711.5037
1.42730.58329381.5024
1.55280.624810051.4994
1.60720.666510721.4975
1.60740.708111391.4920
1.54950.749812061.4904
1.61170.791412731.4883
1.46210.833113401.4850
1.63810.874714071.4838
1.42210.916414741.4813
1.58120.958015411.4789
1.45810.999716081.4750
1.46081.041716751.4800
1.52611.083317421.4798
1.38561.125018091.4796
1.44691.166618761.4766
1.47831.208319431.4741
1.50251.249920101.4733
1.45311.291620771.4726
1.47191.333221441.4712
1.41231.374922111.4700
1.46531.416522781.4673
1.45711.458223451.4660
1.42611.499824121.4660
1.32121.541524791.4620
1.38281.583225461.4617
1.36171.624826131.4597
1.43641.666526801.4567
1.46861.708127471.4549
1.33171.749828141.4530
1.37491.791428811.4506
1.41161.833129481.4468
1.39881.874730151.4456
1.25341.916430821.4448
1.35641.958031491.4412
1.36681.999732161.4392

Framework versions

  • —Transformers 4.51.3
  • —Pytorch 2.4.1+cu121
  • —Datasets 3.5.1
  • —Tokenizers 0.21.1